Dual model speaker identification

ABSTRACT

A processing system receives an audio signal encoding an utterance and determines that a first portion of the audio signal corresponds to a predefined phrase. The processing system accesses one or more text-dependent models associated with the predefined phrase and determines a first confidence based on the one or more text-dependent models associated with the predefined phrase, the first confidence corresponding to a first likelihood that a particular speaker spoke the utterance. The processing system determines a second confidence for a second portion of the audio signal using one or more text-independent models, the second confidence corresponding to a second likelihood that the particular speaker spoke the utterance. The processing system then determines that the particular speaker spoke the utterance based at least in part on the first confidence and the second confidence.

TECHNICAL FIELD

This specification relates to recognizing the identity of a speakerbased on the speaker's voice.

BACKGROUND

In a speech-enabled environment, such as a home or automobile, a usermay access information and/or control various functions using voiceinput. The information and/or functions may be personalized for a givenuser. In multiple user environments, it may therefore be advantageous toidentify a given speaker from among a group of speakers associated withthe speech-enabled environment.

SUMMARY

To determine which user is speaking in a multiuser speech-enabledenvironment, speech-enabled systems may include speaker identificationsystems. Speaker identification systems as described in thisspecification may use a combination of two types of models to identifythe speaker. For a keyword portion of an utterance, the system may useone or more text-dependent models, and for the remainder of theutterance, the system may use one or more text-independent models.Combining these two types of models may provide enhanced accuracy, inparticular during the initial uses of the speaker identification system.

In general, one aspect of the subject matter includes the actions ofreceiving an audio signal encoding an utterance and determining that afirst portion of the audio signal corresponds to a predefined phrase.The actions also include accessing one or more text-dependent modelsassociated with the predefined phrase and determining a first confidencebased on the one or more text-dependent models associated with thepredefined phrase, the first confidence corresponding to a firstlikelihood that a particular speaker spoke the utterance. The actionsfurther include determining a second confidence for a second portion ofthe audio signal using one or more text-independent models, the secondconfidence corresponding to a second likelihood that the particularspeaker spoke the utterance. The actions then include determining thatthe particular speaker spoke the utterance based at least in part on thefirst confidence and the second confidence.

In some implementations, the text-dependent models comprise sets ofmel-frequency cepstral coefficients (MFCCs) associated with thepredefined phrase, each set of MFCCs being associated with an individualspeaker. Such implementations involve comparing the one or more sets ofMFCCs with a set of MFCCs derived from the first portion of the audiosignal to determine the first confidence.

In some implementations, determining a second confidence for a secondportion of the audio signal using one or more text-independent modelsinvolves deriving a set of mel-frequency cepstral coefficients (MFCCs)from the second portion of the audio signal. The determination alsoinvolves accessing one or more Gaussian mixture models (GMMs), each GMMbeing associated with an individual speaker, and processing the set ofMFCCs from the second portion of the audio signal using each of the GMMsto determine the second confidence.

Some implementations involve the additional action of analyzing thefirst portion of the audio signal using the one or more text-independentmodels to determine a third confidence, the third confidencecorresponding to a third likelihood that the particular speakergenerated the utterance. In such implementations, determining that theparticular speaker spoke the utterance is based at least in part on thefirst confidence, the second confidence, and the third confidence.

In some implementations, determining that the particular speaker spokethe utterance includes the actions of combining the first confidence andthe second confidence to generate a combined confidence, and determiningthat the combined confidence for the particular speaker is greater thana combined confidence for any other speaker. Optionally, in suchimplementations combining the first confidence and the second confidencemay include assigning a first weight to the first confidence and asecond weight to the second confidence, the first weight being greaterthan the second weight, and combining the weighted first confidence andthe weighted second confidence to generate the combined confidence.Alternatively or in addition, determining that the combined confidencefor the particular speaker is greater than a combined confidence for anyother speaker may include determining that the combined confidence forthe particular speaker is greater than a combined confidence for anyother speaker and that the combined confidence satisfies a predeterminedthreshold.

In some implementations, determining that the particular speaker spokethe utterance includes determining that the particular speaker fromamong a plurality of speakers spoke the utterance based at least in parton the first confidence and the second confidence.

In some implementations, the actions further include combining the firstconfidence and the second confidence to generate a combined confidenceand determining that the combined confidence for the particular speakeris greater than a threshold. Based on this determination, the actionsthen include initiating an update of a text-dependent model associatedwith the particular speaker, a text-independent model associated withthe particular speaker, or both, using the audio signal encoding theutterance.

The details of the subject matter described in this specification areset forth in the accompanying drawings and the description below. Otherfeatures, aspects, and advantages of the subject matter will becomeapparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example speaker identification system.

FIG. 2 is a flow chart of an example process for speaker identificationusing a combination of text-dependent and text-independent models.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

In a speech-enabled environment, such as a home, automobile, workplace,or school, a user may speak a query or command and a computer-basedsystem may answer the query and/or cause the command to be performed.Such a speech-enabled environment can be implemented using a network ofconnected microphone devices distributed throughout the various rooms orareas of the environment. Through the network of microphones, a user canquery the system without having to have a computer or other device infront of them. In some cases, a user may ask a query of the system,and/or issue a command, that relates to the user's personal information.For example, a particular user might ask the system “when is my meetingwith Matt?” or command the system “remind me to call Matt when I getback home.”

In some instances, the speech-enabled environment may be associated withmultiple users, e.g., all of the people who live in a household. Thismay also apply when a single device is shared by multiple users such asa desktop, laptop, smart television, or tablet. The speech-enabledenvironment may have a limited number of users, e.g., between 2 and 6people in a speech-enabled home or automobile. In such cases, it may bedesirable to determine the identity of the particular user who isspeaking a query. The process of determining a particular speaker may bereferred to as voice recognition, speaker recognition and/or speakeridentification. Speaker identification may allow a user to issue queriesthat act on behalf of a particular user or trigger personalized responsein multi-user environments.

The speech-interface may be triggered using a keyword (e.g., “Google”)that can be used as a basis to perform text-dependent speakeridentification. This text-dependent speaker identification facilitatesvoice recognition based on limited training data. However, performingidentification using a single keyword as training data may bechallenging, in particular when trying to discriminate between speakersusing, for example, approximately 500 ms of audio. To mitigate thesedifficulties, this specification describes a technique of training akeyword-based speaker identification system using two models: atext-dependent model and a text-independent model.

As described in more detail below, when a user registers with thesystem, they speak a registration phrase in a [KEYWORD] [IDENTIFICATIONINFORMATION] format such as “Google, this is John.” The first word“Google” is the keyword and is used as the prefix for future queries.The first of two models—the text-dependent model—is trained using thekeyword from this registration phrase. In some implementations, thistext-dependent model is based on extracting mel-frequency cepstralcoefficient (MFCC) features from the keyword sample and using thesefeatures as a reference for future comparison. As this sample representsa small amount of training data, e.g., less than half a second for somespeakers, it may be advantageous to improve recognition withoutrequiring the speaker to provide more keyword samples. In particular, itmay be desirable to have the speaker utter the registration phrase“Google, this is John” only once.

To that end, the system may use the remainder of the registrationphrase, e.g., “this is John” to train a text-independent model. Thistext-independent model may initially be less reliable than thetext-dependent model, but it is trained on longer sections of speech andmay act as a valuable second signal. Optionally, the system may includethe keyword in the training of the text-independent model. Thetext-independent model may be a Gaussian mixture model (GMM) trainedover the MFCCs for the registration phrase uttered by the user. As aresult of the registration process, for each user, the system may storea set of MFCC vectors that correspond to a text-dependent model, and aGMM that corresponds to a text-independent model.

After registration, when a registered user speaks a query, the systemdetects the presence of the keyword and extracts MFCCs from that segmentof the speech. The system also extracts MFCCs from the remainder of theutterance. Then, for one or more of the users registered with thesystem, the system performs at least two comparisons. The firstcomparison is a text-dependent comparison that compares the set of MFCCsfrom the spoken keyword with the stored keyword's MFCC vectors. Thisprovides a first confidence. The second comparison involves processingthe MFCCs extracted from the remainder of the utterance to compute aconfidence by passing them through the text-independent model. In someimplementations, the keyword may also by processed using thetext-independent model to generate an additional confidence. The systemmay then generate a combined confidence by combining the two (or three)confidences. The combined confidence may weight the confidence scores asdescribed in more detail below. The system then provides a speakeridentification result based on selecting the speaker with the highestcombined confidence, which may also have to meet a minimum threshold.

Once the system has identified a speaker, the system may use theutterance of that speaker to further train the text-dependent and/ortext-independent models. In such cases, the system may have a minimumthreshold for the combined confidence, which may be greater than theminimum threshold for speaker identification.

FIG. 1 illustrates an example speaker identification system 100. Thesystem 100 may be deployed in a speech-enabled environment such as ahome having a living room 116 and a kitchen 118, which is outfitted witha network of microphones 112, 114. The microphones 112, 114 are arrangedin the home such that when a user 102 speaks an utterance 104, at leastone of the microphones 112, 114 will pick up the utterance 104. Themicrophones 112, 114 in turn are connected, via a network 110, to aserver 108, which may be located within the speech-enabled environment,may be remotely located, or may have functionality distributed betweenthe speech-enabled environment and one or more remote locations. Theserver 108 may be computing device or computing devices that take theform of, for example, a standard server, a group of such servers, a rackserver system, a personal computer such as a laptop or desktop computer,a smartphone, a tablet, or any combination of these. The server 108includes a speaker recognition engine 120 that is configured to receivean utterance from a user and perform speaker identification on thereceived utterance.

Speaker identification systems as described in this specification mayuse a keyword. A keyword is a predefined word or phrase that a userspeaks to initiate speech-enabled queries and/or commands. In multipleuser environments, the keyword may be agreed to by all of the users inthe speech-enabled environment. While some implementations discussedelsewhere in this specification discuss detecting a single keyword,implementations are not necessarily limited to detecting one keyword. Infact, some implementations may be used to detect a plurality ofkeywords. The keywords in these implementations may also be shortphrases. Such implementations allow a user to select one of a certainnumber of actions, such as actions presented in a menu, by saying one ofthe menu entries. For example, implementations may use differentkeywords to trigger different actions such as taking a photo, sending anemail, recording a note, and so on.

For example, in the system shown in FIG. 1, the keyword 106 is “Google.”Each time the word “Google” is spoken, it is picked up by one of themicrophones 112, 114, conveyed to the server 108, which performs speechrecognition techniques to determine whether the keyword was spoken. Ifso, the server 108 processes an ensuing command or query. Accordingly,utterances directed at the server 108 may take the general form[KEYWORD] [QUERY], where “KEYWORD” in this example is “Google” and“QUERY” can be any question, command, declaration, or other request thatcan be speech recognized, parsed and acted on by the server 108. Anyother suitable word or phrase may be used as the keyword such as, forexample, “Okay glass.”

In a multi-user, speech-enabled environment 100 such as shown in FIG. 1,in which any of multiple different users may be issuing a query orcommand (e.g., user 102 shown in FIG. 1 could be any of “Matt,” “John,”or “Dominik”), the server 108 may need to identify the user speaking anygiven utterance to properly respond to a command or query. For example,in FIG. 1, user 102 has spoken the utterance 104 “Google: When is mymeeting with Matt?” To answer this query, the server 108 must access thespeaker's online calendar and search it for an upcoming meeting in whichthe name “Matt” appears. But because the speaker of the utterance 104may be any of at least three different users (Matt, John, or Dominik),the server 108 may have difficulty determining, without moreinformation, which user's online calendar to access. Consequently, theserver 108 first determines the identity of the speaker and thenaccesses that user's personal resources, such as his or her onlinecalendar, to respond to the query. To do so, the server 108 may use thekeyword 106 for two purposes: (i) to determine when the server 108 isbeing addressed; and (ii) to determine the identity of the keywordspeaker. In other words, the keyword is used both as a trigger thatinforms the server 108 that it should process a received utterance andas a fixed word for purposes of speaker identification.

Specifically, in conjunction with determining that the keyword “Google”has been uttered by a user (which informs the server 108 that the serveris being addressed), the server 108 also compares the spoken keywordwith stored, previously uttered instances of the keyword by each of theusers in the multi-user environment 100. This keyword comparison may bebased on text-dependent models. The server 108 may also perform speakerrecognition on the utterance 104 using text-independent models asdescribed in more detail below.

To establish the text-dependent and text-independent models associatedwith users, the server 108 may be trained in a registration session.During the registration session, the server 108 creates and/or modifiesa user account for a particular user and associates the user accountwith a text-dependent model and a text-independent model. A registrationsession may involve, among other things, uttering the keyword, e.g.,“Google,” into a microphone 112, 114 and providing other information,e.g., an identification declaration such as “this is John,” sufficientfor the server 108 to associate each user's keyword utterance with theirrespective usernames and thus their respective user accounts.

In an example registration session, the server 108 detects an utterancefrom a user, for example, an introduction query such as “Google: this isJohn.” Next, the server 108 determines (e.g., using speech recognition)that the received utterance includes a keyword (e.g., a keyword, in thisexample “Google”) followed by an introduction declaration (e.g., “thisis John”). The server 108 then compares speaker identification featuresof the uttered keyword with speaker identification features of each of aplurality of previous keyword utterances, each of which corresponds to adifferent known username, each of which in turn corresponds to a knownspeaker. Based on the comparison, the server 108 may determine that theuser associated with the uttered keyword fails to correspond to any ofthe known usernames.

As a result, the server 108 performs speech recognition on theintroduction declaration (e.g., “this is John”) to determine a usernameof the user (e.g., John”). The server 108 then associates the determinedusername, and thus the corresponding user account, with speakeridentification features of the uttered keyword. For example, if theuser's username is determined to be “John,” the system associates theusername “John,” and thus the registered user account associated withthe username John, with speaker identification features of keyword(e.g., “Google”) that was detected.

In the example of FIG. 1, three user accounts have been registered withthe server 108: a first user account associated with a username “Matt”,a second user account associated with a username “John”, and a thirduser account associated with the username “Dominik.” Each account may beassociated with, and provide its respective owner with access to, acollection of personal resources such as the account owner's contactlist, calendar, email, voicemail, social networks, biographicalinformation, financial information, applications and the like. Access tosuch personal resources can be controlled locally by the server 108 orcan be distributed, in whole or in part, across one or more servercomputer systems.

During, or as a result of, the registration session, the server 108derives a set of MFCCs from the uttered keyword, and stores the MFCC'sin association with the user's account (e.g., in the storage device128). Alternatively or in addition, the server 108 may derive a GMM fromthe uttered keyword. This set of MFCC's and/or GMM corresponds to thetext-dependent model for the user. During, or as a result of, theregistration session, the server 108 also derives a text-independentmodel from the user's utterance and stores the text and dependent modelin association with the user's account (e.g., in the storage device132). The server 108 may derive the text-independent model from theentire user's utterance (e.g., “Google: this is Matt”), or from only theportion of the utterance following the keyword (e.g., “this is Matt”).The text-independent model may be, for example, a GMM, a hidden Markovmodel (HMM), or any other model suitable for text-independent speakerrecognition.

Following the registration session, each time one of the users in thespeech-enabled environment utters the keyword “Google,” the server 108can classify the speaker by performing speaker identification using thetext-dependent models associated with the keyword. In addition, theserver 108 can classify the speaker by performing voice recognition onthe utterance using text-independent models that are derived from theinitial registration session. By combining the classifications obtainedusing the text-dependent models and the text-independent models, theserver 108 can achieve more accurate identification of the speaker.After identifying the speaker, the server 108 may then give the useraccess to his or her account and the related resources.

In operation, the speaker recognition engine 120 first receives an audiosignal 122 corresponding to the encoded utterance 104 from the user 102.The audio signal 122 may be an analog or digital representation of soundin the environment of an embodiment that is captured by a microphone112, 114. The speaker recognition engine 120 includes several modulesfor performing speaker recognition. Modules may be any combination ofsoftware, firmware, and/or hardware components configured to perform thefunctions described herein. The modules include a keyword detectormodule 124, a text-dependent analyzer module 126, a text-independentanalyzer module 130, and the speaker classifier module 134. The speakerrecognition engine also includes or has access to storage device 128 andstorage device 132, which stored text-dependent in text-independentmodels associated with user accounts. The storage devices 128, 132, maybe a memory, hard disk drive, or any other suitable storage device, andmay be local to the server 108 or may be remotely located.

Upon receiving the audio signal 122, the keyword detector module 124processes the audio signal 122 to determine whether it includes akeyword. For example, the keyword detector module 124 may perform speechrecognition on the audio signal 122 to identify a portion of the signalcorresponding to the keyword. In some implementations, the keyworddetector module 124 may use speaker-agnostic speech recognition modelssuch as HMMs to identify the keyword. When the keyword is identified,the keyword detector 124 communicates the portion of the audio waveform122 associated with the keyword to the text-dependent analyzer 126.

The text-dependent analyzer module 126 analyzes the portion of the audiosignal 122 that corresponds to the keyword. In particular, thetext-dependent analyzer module 126 determines a confidence levelassociated with one or more text-dependent models stored in a memory 128that is accessible to the speaker recognition engine 120. Each of thetext-dependent models is associated with a user account that isregistered with the speech-enabled environment. For example, the “Matt”user account is associated with a text-dependent model 140 a, the “John”user account is associated with a text-dependent model 142 a, and the“Dominik” user account is associated with a text-dependent model 144 a.

To determine the confidence level associated with each of thetext-dependent models, the text-dependent analyzer module 126 comparesspeaker identification features (e.g., MFCC features, which collectivelycan form a feature vector) of the uttered keyword with speakeridentification features of each of a one or more previous utterances ofthe keyword that are associated with user accounts registered with theserver 108. In some implementations, each of the previous keywordutterances corresponds to a different known speaker (e.g., known to, andhaving a corresponding username and account on, the server 108 in FIG.1). Alternatively or in addition, the server 108 can collect andmaintain (and use in the speaker recognition evaluation) two or moreinstances of utterances of the keyword for each known speaker. Forexample, during operations the server 108 can store additional instancesof the keyword that it determines have been uttered by a particular userin association with that user's account in a retraining process.Advantageously, a speaker identification process that has availablemultiple examples of the target keyword (against which to compare thecurrent, uttered word) may be more accurate and robust.

In particular, the text-dependent analyzer module 126 determines howclosely the speaker identification features of the uttered keyword matchthe speaker identification features of one of the stored instances ofthe keyword. For example, in some implementations, the text-dependentanalyzer module 126 may perform dynamic time warping (DTW) on the MFCCsfrom the uttered keyword. As a result of performing DTW, thetext-dependent analyzer module 126 determines one or more confidencelevels corresponding to likelihoods that a particular speaker spoke theutterance. Alternatively or in addition, in some implementations thetext-dependent analyzer model 126 may analyze the MFCCs from the utteredkeyword using a GMM trained on previous utterances of the keyword by theuser.

The confidence level in some implementations may be a normalized valuebetween 0.0 and 1.0. For example, based on the DTW analysis, thetext-dependent analyzer module 126 may determine a confidence level of0.60 associated with the user account “Matt,” a confidence level of 0.90associated with the user account “John,” and a confidence level of 0.30associated with the user account “Dominik.” As discussed above, eachuser account may have one or more stored instances of the keywordassociated with it, and the confidence level may be for the closestmatching instance of the keyword. Alternatively or in addition, thetext-dependent analyzer module 126 may extract the MFCCs from theuttered keyword, compute an average MFCC and then perform a nearestneighbor analysis between the average MFCC of the uttered keyword witheach of the plurality of previous utterances of the keyword. The nearestprevious keyword utterance, provided it is within a threshold maximumallowed distance, may be determined to match the uttered keyword.

Next, a text-independent analyzer module 130 analyzes the audio signal122. In particular, the text-independent analyzer module 130 determinesa confidence level for one or more text-independent models stored in thestorage device 132, where each of the text-independent models isassociated with a user account. In some implementations, thetext-independent analyzer module 130 may analyze only the portion of theaudio signal 122 subsequent to the portion of the audio signal thatcorresponds to the keyword. In other words, if the keyword is “Google,”then the text-independent analyzer module 130 would only analyze theportion of the audio signal after the term “Google.” Alternatively or inaddition, the text-independent analyzer module 130 may also analyze thekeyword using text-independent models. In such implementations, thetext-independent analyzer module 130 may output two confidence levelsfor each text-independent model, i.e., one confidence level associatedwith the portion of the audio signal 122 corresponding to the keywordand another confidence level associated with the subsequent portion ofthe audio signal 122. Moreover, in some implementations, thetext-independent analyzer module 130 may analyze the entire audio signal122 using the text-independent models. In such cases, thetext-independent analyzer module 130 would again output a singleconfidence level for each text-independent model.

In the example of FIG. 1, the “Matt” user account is associated with atext-independent model 140 b, the “John” user account is associated witha text-independent model 142 b, and the “Dominik” user account isassociated with the text-independent model 144 b. As discussed above,the text-independent models may be GMMs, HMMs, or any other suitablemodels. The confidence level in some implementations may be a normalizedvalue between 0.0 and 1.0. For example, based on the text-independentmodels, the text-independent analyzer module 130 may determine aconfidence level of 0.45 associated with the user account “Matt,” aconfidence level of 0.85 associated with the user account “John,” and aconfidence level of 0.20 associated with the user account “Dominik.” Asdiscussed above, each user account may have one or more text-independentmodels associated with it, and the confidence level may be the highestconfidence associated with a particular user account.

Finally, the speaker classifier 134 receives confidence data from thetext-dependent analyzer 126 and the text-independent analyzer 130 andmakes a determination as to the identity of the user 102. In particular,the speaker classifier 134 receives a confidence level based on one ormore text-dependent models from the text-dependent analyzer 126. Forexample, the text-dependent analyzer 126 may provide the speakerclassifier 134 with a confidence level of 0.90 associated with the useraccount “John.” This confidence level corresponds to a likelihood of 90%that the user 102 was the user associated with the account “John.”Likewise, speaker classifier 134 receives a confidence level based onone or more text-independent models from the text-independent analyzer130. For example, the text-independent analyzer 130 may provide thespeaker classifier 134 with a confidence level of 0.85 associated withthe user account “John.” This confidence level corresponds to alikelihood of 85% that the user 102 was the user associated with theaccount “John.” In some implementations, the speaker classifier 134 mayreceive two or more confidence levels for a given text-independentmodel, for example in cases where the text-independent analyzer module130 provides multiple confidence levels for a text-independent model asdescribed above. The multiple confidence levels for the text-independentmodel may be combined to form an average confidence level for thetext-independent models. This average may be a weighted averageconfidence level for the text-independent models.

The speaker classifier 134 then combines the confidence levels from thetext-dependent models in the text-independent models to make a finaldetermination as to the identity of the user 102. For example, thespeaker classifier 134 may average the confidence levels associated witha given user account. Continuing the above example, the combinedconfidence level associated with the user account “John” would be 0.875.

In some implementations, the speaker classifier 134 may perform aweighted average of the confidence levels to obtain a final, combined,confidence level. For example, the confidence level associated with thetext-dependent model may be weighted more heavily than the confidencelevel associated with the text-independent model (e.g., the confidencelevel of the text-dependent model could be multiplied by a weight of0.75 while the confidence level of the text-independent model could bemultiplied by weight of 0.25). Alternatively, the confidence levelassociated with the text-independent model may be weighted more heavilythan the confidence level associated with the text-dependent model(e.g., the confidence level of the text-dependent model could bemultiplied by a weight of 0.25 while the confidence level of thetext-independent model could be multiplied by weight of 0.75).

In some implementations, the weighting associated with thetext-dependent and text-independent models may vary over time. Inparticular, the confidence level associated with the text-dependentmodel may initially be weighted more heavily than the confidence levelassociated with the text-independent model. This initial weighting mayreflect the higher accuracy of the text-dependent model during initialoperations. But over time, as the text-independent models are trained bysubsequent utterances of users, the text-independent models may becomemore accurate and may be weighted more heavily than the text-dependentmodels. The server 108 may change the weighting between thetext-dependent models in the text-independent models based on a numberof utterances by a user associated with the models. Specifically, theserver 108 may weight the text-independent models more heavily after theserver determines that a given number of utterances have been processedby a particular user. For example, after 10, 15, 20, or 25 utterances bya user associated with a particular user account, the server 108 mayincrease the weighting given to text-independent models associated withthat user account. In some implementations, the server 108 may increasethe weighting multiple times as it processes additional utterances by agiven user.

Based on the combined confidence level, the speaker classifier 134determines whether the speaker 102 of the utterance 104 is associatedwith any of the user accounts that are registered with the server 108.To continue the example above, assuming that the combined confidencelevel associated with the user account “John” is 0.85, and also thatthis combined confidence level is the highest confidence level for allof the user accounts registered with the server 108, the speakerclassifier 134 may identify the speaker 102 as the user “John.”

In some implementations, the speaker classifier 134 may apply a minimumthreshold such as, for example, 0.50, 0.40, 0.30, or 0.20. If thecombined confidence level fails to exceed the minimum threshold, thespeaker classifier 134 may determine that the speaker 102 of theutterance 104 is not associated with any user account that is registeredwith the server 108. In such an instance, the server 108 may provide anindication to the user 102 that the speaker was not recognized. Theserver 108 may then provide an additional opportunity for the user 102to speak the voice command.

Optionally, if the keyword was not successfully speaker-identified (andassuming that the associated query requires personal information orother user-specific resources to satisfy), the server 108 can challengethe user for his or her identity, e.g., by asking who has spoken theutterance detected. The server 108 can then use speech recognition toanalyze the user's response (e.g., “this is Matt”) to determine that theuser is Matt and subsequently fulfill the query using Matt's personalinformation or other user-specific resources.

In an example, the server 108 may determine that the user associatedwith the uttered keyword fails to correspond to any of the knownusernames. This situation could happen, for example, if the server 108is new or has been reconfigured or if ambient noise or the likeinterferes with the voice recognition of the uttered keyword. As aresult of the failure to identify the user, the server 108 prompts theuser to make an identification utterance (e.g., using synthesized voiceoutput the system states “who are you?” or “state your name”). Theserver 108 then performs speech recognition on the identificationutterance made in response to the prompting to determine a username ofthe user. For example, if in response to the prompt the user responded“this is Matt” or simply “Matt,” the server 108 could determine that theword “Matt” was spoken by the user and assume that the user had justspoken his username. The server 108 then performs a registration sessionon the utterance for the user as described above. Going forward, thesystem will then be able to identify Matt when he speaks the keywordand, in response, give him access to his account and its relatedresources.

After determining the identity of the speaker, the server 108 mayprovide the speaker that made the utterance with access to one or moreresources associated with the speaker. For example, if the speakerrecognition engine 120 determined that the speaker identificationfeatures of the user's “Google” utterance sufficiently matched those ofa previous utterance of the word “Google” by John, then the server 108would decide that the user that spoke the utterance 104 was the userwith the username “John” and thus would grant that user access to theresources associated with John's account registered on the server 108.As a result, the command or query following John's utterance of thekeyword “Google” would be handled based on the context that the speakeris John and that John's personal information and other account resourcesrepresent the relevant body of information.

Once the speaker recognition engine 120 has identified a particularspeaker, the server 108 may use the utterance of that speaker to furthertrain the text-dependent and/or text-independent models. For example,the server 108 may store MFCCs from the portion of the audio signal 122as an additional text-dependent model associated with the user accountin the storage device 128. Alternatively or in addition, the server 108may perform additional training of the text-independent model (e.g., theGMM) associated with that speaker using the MFCCs from the entire audiosignal 122 and/or the remainder of the audio signal 122. In such cases,the server 108 may require the combined confidence for the utterance toexceed a minimum threshold to trigger the retraining, which may begreater than the minimum threshold for speaker identification.

Variations on the techniques described above may be implemented. Forexample, any appropriate keyword may be used as desired and the formatof the utterances to the system need not necessarily conform to theformat [KEYWORD] [QUERY]. Potentially, the keyword may occur at anylocation within the utterance. In addition, to enhance system security,the system could implement a verification step to further confirm thespeaker's identity (that is, in addition to performing voice recognitionon the spoken keyword). For example, the system could ask the user forthe name of a person to whom an email was sent from the purported user'saccount within the past 24 hours. Moreover, recognition of the keywordand recognition of the speaker's identity can be performed independentlyof each other and potentially at different locations (e.g., the keywordcan be recognized at the local system and the speaker can be recognizedat a remote server or vice versa). Similarly, fulfillment of the queryor command can be performed at the local system or at a remote server ora combination of the two.

FIG. 2 shows an example process 200 for speaker identification using acombination of text-dependent and text-independent models. In thecontext of FIG. 1, the process 200 can be performed in whole or part atthe server 108, at one or more other servers, or distributed among thoselocations.

In step 202, the server receives an audio signal encoding an utterance.Next, in step 204, the server determines that a first portion of theaudio signal corresponds to a predefined phrase (e.g., a keyword). Thepredefined phrase may be one or more words that are spoken by usersduring a registration session.

The server then accesses one or more text-dependent models associatedwith the predefined phrase in step 206. In some implementations, the oneor more text-dependent models may be one or more sets of MFCCsassociated with the predefined phrase, where each set of MFCC's isassociated with an individual speaker.

Then, in step 208, the server determines the first confidence based onthe one or more text-dependent models associated with the predefinedphrase. The first confidence corresponds to the first likelihood that aparticular speaker spoke the utterance. The particular speaker may beone speaker from among a plurality of speakers that are registered withthe server. In some implementations, the server may determine the firstconfidence by comparing one or more sets of MFCC's with a set of MFCC'sderived from the first portion of the audio signal.

In step 210, the server determines a second confidence for a secondportion of the audio signal using one or more text-independent models.The second confidence corresponds to a second likelihood that theparticular speaker spoke the utterance. The second portion of the audiosignal may be, for example, a portion of the audio signal subsequent tothe portion of the audio signal corresponding to the predefined phrase.In some implementations, the server derives a set of MFCC's from thesecond portion of the audio signal. The server may then access one ormore GMMs, where each GMM is associated with an individual speaker,i.e., each GMM corresponds to a text-independent model that isassociated with a particular user account. The server may then processthe set of MFCCs derived from the second portion of the audio signalusing each of the GMMs to determine the second confidence.

Some implementations also involve the server analyzing the portion ofthe audio signal including the predefined phrase using one or moretext-independent models to determine a third confidence. The thirdconfidence corresponds to a third likelihood that a particular speakergenerated the utterance.

Finally, in step 212, the server determines that the particular speakerspoke the utterance based at least in part on the first confidence andthe second confidence. In particular, the server may combine the firstconfidence and the second confidence to generate a combined confidence.This combined confidence may be an average or weighted average of thefirst and second confidence. Where the combined confidence is a weightedaverage, the first confidence (i.e., corresponding to the text-dependentmodel) may be weighted more heavily than the second confidence (i.e.,corresponding to the text-independent model). Alternatively, in someimplementations, the second confidence may be weighted more heavily thanthe first confidence. Furthermore, in some implementations, theweighting may change over time, e.g., weighting the text-independentmodel more heavily as the server processes more utterances from aparticular user. In some cases, the server may determine that thecombined confidence for the particular speaker is greater than acombined confidence for any other speaker that is registered with theserver. The server also may determine that the combined confidence forthe particular speaker satisfies a predetermined threshold. Inimplementations that include a third confidence level, the server maymake the determination based on the first, second, and third confidencelevels.

Some implementations further involve updating the text-dependent modeland/or the text-independent models associated with the particularspeaker when the confidence for the speaker satisfies a threshold. Thisthreshold may be different than (e.g., higher than) the threshold fordetermining that the particular speaker spoke the utterance. In suchimplementations, the server may combine the first confidence and thesecond confidence to generate a combined confidence and determinewhether the combined confidence for the particular speaker is greaterthan a threshold for updating the text-dependent and/or text-independentmodels for the particular speaker. In some aspects, the text-dependentmodel and the text-independent models may have separate thresholds fortriggering an update. These separate thresholds may depend on thecombined confidence. Alternatively or in addition, the server mayanalyze the confidence for the text-dependent models to trigger anupdate of the text-dependent models and analyze the confidence for thetext-independent models to trigger updates of the text-independentmodels. If the confidence is greater than the threshold, the server mayinitiate an update of a text-dependent model associated with theparticular speaker, a text-independent model associated with theparticular speaker, or both, using the audio signal encoding theutterance. For example, the server may store MFCCs from the portion ofthe audio signal as an additional text-dependent model associated withthe user account of the particular speaker. Alternatively or inaddition, the server may perform additional training of thetext-independent model (e.g., the GMM) associated with that speakerusing the MFCCs from the audio signal. Further, the server may transmitthe audio signal to another processing system to perform an update ofthe text-dependent and/or text-independent models.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a processing system on data stored on one ormore computer-readable storage devices or received from other sources.

The term “processing system” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a speech-enabled home device of an automated speakeridentification system that includes the speech-enabled home device thatincludes one or more microphones for detecting utterances spoken in ahome environment, and a server based speaker recognition engine that isassociated with an automated query processor and that includes (i) oneor more text-dependent speaker identification models that are trainedusing multiple previous utterances of a keyword by a particular speakerand by other users whose accounts are registered with the server, and(ii) one or more text-independent speaker identification models that aretrained using utterances of words other than the keyword by theparticular speaker and by other users whose accounts are registered withthe server, an audio signal encoding an utterance that was spoken in ahome environment and was detected by one or more microphones of thespeech-enabled home device, and that includes the keyword and a query;determining, by the server-based speaker recognition engine and based onan analysis of a portion of the audio signal that corresponds to thekeyword by one or more of the text-dependent speaker identificationmodels that are trained using utterances of the keyword by theparticular speaker and by the other users whose accounts are registeredwith the server, a first speaker identification confidence value thatreflects a likelihood that the particular speaker spoke the keyword;determining, by the server-based speaker recognition engine and based onan analysis of at least a portion of the audio signal that correspondsto the query by one or more of the text-independent speakeridentification models that are trained using utterances of words otherthan the keyword by the particular speaker and by other users whoseaccounts are registered with the server, a second speaker identificationconfidence value that reflects a likelihood that the particular speakerspoke the query; determining, by the server-based speaker recognitionengine, a first quantity of the utterances of the keyword by theparticular speaker that were used to train the one or moretext-dependent speaker identification models; determining, by theserver-based speaker recognition engine, a second quantity of theutterances of the words other than the keyword by the particular speakerthat were used to train the one or more text-independent speakeridentification models; assigning, by the server-based speakerrecognition engine, a first weight to the first speaker identificationconfidence value based at least on the first quantity of utterances ofthe keyword by the particular speaker that were used to train the one ormore text-dependent speaker identification models, and a second weightto the second speaker identification confidence value based at least onthe second quantity of utterances of the words other than the keyword bythe particular speaker that were used to train the one or moretext-independent speaker identification models; determining, by theserver-based speaker recognition engine, that the particular speakerspoke the utterance encoded in the audio signal based at least in parton the weighted first speaker identification confidence value and theweighted second speaker identification confidence value; in response todetermining by the server-based speaker recognition engine that theparticular speaker spoke the utterance encoded in the audio signal basedat least in part on the weighted first speaker identification confidencevalue and the weighted second speaker identification confidence value,initiating access to one or more account resources associated with theparticular speaker for preparation of a personalized response to thequery by the automated query processor that is associated with theserver-based speaker recognition engine; and providing, by the automatedquery processor that is associated with the server-based speakerrecognition engine, the personalized response to the speech-enabled homedevice for output to the particular speaker.
 2. The method of claim 1,comprising: obtaining one or more sets of mel-frequency cepstralcoefficients (MFCCs) associated with the keyword, each set of MFCCsbeing associated with an individual speaker; and wherein determining,based on an analysis of a portion of the audio signal that correspondsto the keyword by one or more of the more text-dependent speakeridentification models that are trained using utterances of the keywordby the particular speaker, the first speaker identification confidencevalue that reflects the likelihood that the particular speaker spoke thekeyword, comprises determining, based on a comparison of the one or moresets of MFCCs to a set of MFCCs derived from the portion of the audiosignal that corresponds to the keyword, a first speaker identificationconfidence value that reflects a likelihood that the particular speakerspoke the keyword.
 3. The method of claim 1, wherein determining thesecond speaker identification confidence value comprises: deriving a setof mel-frequency cepstral coefficients (MFCCs) from the portion of theaudio signal that corresponds to the query; accessing one or moreGaussian mixture models (GMMs), each GMM being associated with anindividual speaker; and processing the set of MFCCs from the portion ofthe audio signal that corresponds to the query using each of the GMMs todetermine the second speaker identification confidence value.
 4. Themethod of claim 1 further comprising: analyzing the portion of the audiosignal that corresponds to the keyword using the one or moretext-independent models to determine a third speaker identificationconfidence value that reflects a likelihood that the particular speakergenerated the utterance; and wherein determining that the particularspeaker spoke the utterance based at least in part on the weighted firstconfidence and the weighted second confidence comprises determining thatthe particular speaker spoke the utterance based at least in part on theweighted first confidence, the weighted second confidence, and the thirdspeaker identification confidence value.
 5. The method of claim 1,wherein determining that the particular speaker spoke the utterancebased at least on the weighted first speaker identification confidencevalue and the weighted second speaker identification confidence valuecomprises: combining the weighted first speaker identificationconfidence value and the weighted second speaker identificationconfidence value to generate a combined confidence; and determining thatthe combined confidence for the particular speaker is greater than acombined confidence for any other speaker.
 6. The method of claim 5,wherein determining that the combined confidence for the particularspeaker is greater than a combined confidence for any other speakercomprises determining that the combined confidence for the particularspeaker is greater than a combined confidence for any other speaker andthat the combined confidence satisfies a predetermined threshold.
 7. Themethod of claim 1, wherein determining that the particular speaker spokethe utterance based at least in part on the weighted first speakeridentification confidence value and the weighted second speakeridentification confidence value comprises determining that theparticular speaker from among a plurality of speakers spoke theutterance based at least in part on the weighted first speakeridentification confidence value and the weighted second speakeridentification confidence value.
 8. The method of claim 1, furthercomprising: combining the weighted first speaker identificationconfidence value and the weighted second speaker identificationconfidence value to generate a combined confidence; determining that thecombined confidence for the particular speaker is greater than athreshold; and initiating an update of the one or more text-dependentmodels that are trained using utterances of the keyword by theparticular speaker, the one or more text-independent models that aretrained using utterances of words other than the keyword by theparticular speaker, or both, using the audio signal encoding theutterance that includes the keyword and the query.
 9. The method ofclaim 1, wherein the second weight increases or the first weightdecreases as the first quantity of utterances or the second quantity ofutterances increases.
 10. A system comprising: one or more computers andone or more storage devices storing instructions that are operable, whenexecuted by the one or more computers, to cause the one or morecomputers to perform operations comprising: receiving, by aspeech-enabled home device of an automated speaker identification systemthat includes the speech-enabled home device that includes one or moremicrophones for detecting utterances spoken in a home environment, and aserver based speaker recognition engine that is associated with anautomated query processor and that includes (i) one or moretext-dependent speaker identification models that are trained usingmultiple previous utterances of a keyword by a particular speaker and byother users whose accounts are registered with the server, and (ii) oneor more text-independent speaker identification models that are trainedusing utterances of words other than the keyword by the particularspeaker and by other users whose accounts are registered with theserver, an audio signal encoding an utterance that was spoken in a homeenvironment and was detected by one or more microphones of thespeech-enabled home device, and that includes the keyword and a query;determining, by the server-based speaker recognition engine and based onan analysis of a portion of the audio signal that corresponds to thekeyword by one or more of the text-dependent speaker identificationmodels that are trained using utterances of the keyword by theparticular speaker and by the other users whose accounts are registeredwith the server, a first speaker identification confidence value thatreflects a likelihood that the particular speaker spoke the keyword;determining, by the server-based speaker recognition engine and based onan analysis of at least a portion of the audio signal that correspondsto the query by one or more of the text-independent speakeridentification models that are trained using utterances of words otherthan the keyword by the particular speaker and by other users whoseaccounts are registered with the server, a second speaker identificationconfidence value that reflects a likelihood that the particular speakerspoke the query; determining, by the server-based speaker recognitionengine, a first quantity of the utterances of the keyword by theparticular speaker that were used to train the one or moretext-dependent speaker identification models; determining, by theserver-based speaker recognition engine, a second quantity of theutterances of the words other than the keyword by the particular speakerthat were used to train the one or more text-independent speakeridentification models; assigning, by the server-based speakerrecognition engine, a first weight to the first speaker identificationconfidence value based at least on the first quantity of utterances ofthe keyword by the particular speaker that were used to train the one ormore text-dependent speaker identification models, and a second weightto the second speaker identification confidence value based at least onthe second quantity of utterances of the words other than the keyword bythe particular speaker that were used to train the one or moretext-independent speaker identification models; determining, by theserver-based speaker recognition engine, that the particular speakerspoke the utterance encoded in the audio signal based at least in parton the weighted first speaker identification confidence value and theweighted second speaker identification confidence value; in response todetermining by the server-based speaker recognition engine that theparticular speaker spoke the utterance encoded in the audio signal basedat least in part on the weighted first speaker identification confidencevalue and the weighted second speaker identification confidence value,initiating access to one or more account resources associated with theparticular speaker for preparation of a personalized response to thequery by the automated query processor that is associated with theserver-based speaker recognition engine; and providing, by the automatedquery processor that is associated with the server-based speakerrecognition engine, the personalized response to the speech-enabled homedevice for output to the particular speaker.
 11. The system of claim 10,comprising: obtaining one or more sets of mel-frequency cepstralcoefficients (MFCCs) associated with the keyword, each set of MFCCsbeing associated with an individual speaker; and wherein determining,based on an analysis of a portion of the audio signal that correspondsto the keyword by one or more of the more text-dependent speakeridentification models that are trained using utterances of the keywordby the particular speaker, the first speaker identification confidencevalue that reflects the likelihood that the particular speaker spoke thekeyword, comprises determining, based on a comparison of the one or moresets of MFCCs to a set of MFCCs derived from the portion of the audiosignal that corresponds to the keyword, a first speaker identificationconfidence value that reflects a likelihood that the particular speakerspoke the keyword.
 12. The system of claim 10, wherein determining thesecond speaker identification confidence value comprises: deriving a setof mel-frequency cepstral coefficients (MFCCs) from the portion of theaudio signal that corresponds to the query; accessing one or moreGaussian mixture models (GMMs), each GMM being associated with anindividual speaker; and processing the set of MFCCs from the portion ofthe audio signal that corresponds to the query using each of the GMMs todetermine the second speaker identification confidence value.
 13. Thesystem of claim 10 wherein the operations further comprise: analyzingthe portion of the audio signal that corresponds to the keyword usingthe one or more text-independent models to determine a third speakeridentification confidence value that reflects a likelihood that theparticular speaker generated the utterance; and wherein determining thatthe particular speaker spoke the utterance based at least in part on theweighted first confidence and the weighted second confidence comprisesdetermining that the particular speaker spoke the utterance based atleast in part on the weighted first confidence, the weighted secondconfidence, and the third speaker identification confidence value. 14.The system of claim 10, wherein determining that the particular speakerspoke the utterance based at least on the weighted first speakeridentification confidence value and the weighted second speakeridentification confidence value comprises: combining the weighted firstspeaker identification confidence value and the weighted second speakeridentification confidence value to generate a combined confidence; anddetermining that the combined confidence for the particular speaker isgreater than a combined confidence for any other speaker.
 15. The systemof claim 14, wherein determining that the combined confidence for theparticular speaker is greater than a combined confidence for any otherspeaker comprises determining that the combined confidence for theparticular speaker is greater than a combined confidence for any otherspeaker and that the combined confidence satisfies a predeterminedthreshold.
 16. The system of claim 10, wherein determining that theparticular speaker spoke the utterance based at least in part on theweighted first speaker identification confidence value and the weightedsecond speaker identification confidence value comprises determiningthat the particular speaker from among a plurality of speakers spoke theutterance based at least in part on the weighted first speakeridentification confidence value and the weighted second speakeridentification confidence value.
 17. The system of claim 10, wherein theoperations further comprise: combining the weighted first speakeridentification confidence value and the weighted second speakeridentification confidence value to generate a combined confidence;determining that the combined confidence for the particular speaker isgreater than a threshold; and initiating an update of the one or moretext-dependent models that are trained using utterances of the keywordby the particular speaker, the one or more text-independent models thatare trained using utterances of words other than the keyword by theparticular speaker, or both, using the audio signal encoding theutterance that includes the keyword and the query.
 18. A non-transitorycomputer-readable medium storing software comprising instructionsexecutable by one or more computers which, upon such execution, causethe one or more computers to perform operations comprising: receiving,by a speech-enabled home device of an automated speaker identificationsystem that includes the speech-enabled home device that includes one ormore microphones for detecting utterances spoken in a home environment,and a server based speaker recognition engine that is associated with anautomated query processor and that includes (i) one or moretext-dependent speaker identification models that are trained usingmultiple previous utterances of a keyword by a particular speaker and byother users whose accounts are registered with the server, and (ii) oneor more text-independent speaker identification models that are trainedusing utterances of words other than the keyword by the particularspeaker and by other users whose accounts are registered with theserver, an audio signal encoding an utterance that was spoken in a homeenvironment and was detected by one or more microphones of thespeech-enabled home device, and that includes the keyword and a query;determining, by the server-based speaker recognition engine and based onan analysis of a portion of the audio signal that corresponds to thekeyword by one or more of the text-dependent speaker identificationmodels that are trained using utterances of the keyword by theparticular speaker and by the other users whose accounts are registeredwith the server, a first speaker identification confidence value thatreflects a likelihood that the particular speaker spoke the keyword;determining, by the server-based speaker recognition engine and based onan analysis of at least a portion of the audio signal that correspondsto the query by one or more of the text-independent speakeridentification models that are trained using utterances of words otherthan the keyword by the particular speaker and by other users whoseaccounts are registered with the server, a second speaker identificationconfidence value that reflects a likelihood that the particular speakerspoke the query; determining, by the server-based speaker recognitionengine, a first quantity of the utterances of the keyword by theparticular speaker that were used to train the one or moretext-dependent speaker identification models; determining, by theserver-based speaker recognition engine, a second quantity of theutterances of the words other than the keyword by the particular speakerthat were used to train the one or more text-independent speakeridentification models; assigning, by the server-based speakerrecognition engine, a first weight to the first speaker identificationconfidence value based at least on the first quantity of utterances ofthe keyword by the particular speaker that were used to train the one ormore text-dependent speaker identification models, and a second weightto the second speaker identification confidence value based at least onthe second quantity of utterances of the words other than the keyword bythe particular speaker that were used to train the one or moretext-independent speaker identification models; determining, by theserver-based speaker recognition engine that the particular speakerspoke the utterance encoded in the audio signal based at least in parton the weighted first speaker identification confidence value and theweighted second speaker identification confidence value; in response todetermining by the server-based speaker recognition engine that theparticular speaker spoke the utterance encoded in the audio signal basedat least in part on the weighted first speaker identification confidencevalue and the weighted second speaker identification confidence value,initiating access to one or more account resources associated with theparticular speaker for preparation of a personalized response to thequery by the automated query processor that is associated with theserver-based speaker recognition engine; and providing, by the automatedquery processor that is associated with the server-based speakerrecognition engine, the personalized response to the speech-enabled homedevice for output to the particular speaker.